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Modified DSFD and TCDCN Based Facial Landmark Detection for Gender and Age Classification

  • ANALYSIS AND SYNTHESIS OF SIGNALS AND IMAGES
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Optoelectronics, Instrumentation and Data Processing Aims and scope

Abstract

Facial analysis evaluates the physical appearances of person, which is crucial for several clinical settings. The perspective of real-world faces captured in an uncontrolled environment makes it harder for the gender prediction algorithm to correctly identify gender. The accuracy of the most advanced algorithms currently in use for real-time facial gender prediction is decreased by these factors. Most importantly the facial gender prediction can pave a way for the visually challenged persons to identify the gender and age. A dual shot face detector with task restricted Fine-tuned deep neural network (DTFN) is created to recognise the facial land markings for accurate gender and age prediction in order to overcome the challenges and defects. Bidirectional filtering and sigmoid stretching are the main preprocessing methods used to improve contrast and remove noise from the input image once facial photographs are first gathered. Next, employing the modified dual shot face detector (DSFD) to separate the face from the remaining background image. To solve this problem, DSFD is built around caps net. A task constrained deep convolutional neural network (TCDCN) is then used to extract and identify features from facial landmarks. The collected features are fed into a fine tuned deep neural network (DNN) classifier, which further classifies the data according to age and gender. By adjusting the hidden layer’s parameter using the stochastic gradient descent technique, fine tweaking is achieved. According to the results of experimental research the proposed technique achieves 96\(\%\) accuracy, precision has 96.57\(\%\), and hit rate value of 96\(\%\). Thus, the proposed approach is the best option for automatic facial land mark detection.

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Funding

This work was supported by ongoing institutional funding. No additional grants to carry out or direct this particular research were obtained.

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Authors and Affiliations

  1. Department of Computer Science, Vels Institute of Science, Technology and Advanced Studies (VISTAS), 600117, Pallavaram, Chennai, India

    J. Meenakshi & G. Thailambal

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  1. J. Meenakshi
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  2. G. Thailambal
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The corresponding author claims the major contribution of the paper including formulation, analysis and editing. The co-author provides guidance to verify the analysis result and manuscript editing.

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Correspondence to J. Meenakshi or G. Thailambal.

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The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

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Meenakshi, J., Thailambal, G. Modified DSFD and TCDCN Based Facial Landmark Detection for Gender and Age Classification. Optoelectron.Instrument.Proc. 60, 398–411 (2024). https://doi.org/10.3103/S8756699024700468

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